Biometric information processing apparatus, biometric information processing method

ABSTRACT

A biometric information processing apparatus includes a biometric sensor configured to acquire biometric information of a first instance, a second instance and a third instance; a processor configured to execute a procedure, the procedure comprising: extracting an authentication feature used for matching from the biometric information of each of the second instance and the third instance; normalizing the relative positions of authentication features of the second instance and the third instance by using the biometric information of the first instance; and extracting a relative feature indicating a relative positional relationship between the authentication features of the second instance and the third instance normalized in the normalizing procedure.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2012-068093, filed on Mar. 23,2012, the entire contents of which are incorporated herein by reference.

FIELD

The present disclosure relates to a biometric information processingapparatus, a biometric information processing method and acomputer-readable recording medium storing a biometric informationprocessing program.

BACKGROUND

Recently, the number of users for a single system that uses biometricauthentication continues to increase. In order to suppress erroneousauthentication despite the increasing number of users for a singlesystem, improved performance for discrimination of an individual isdesired. For example, International Publication Pamphlet No. WO2005/069212 discloses the use in authentication of the positionalrelationship between fingerprints of several fingers. In addition,Japanese Laid-open Patent Publication No. 2003-281508 discloses anapparatus equipped with a plurality of fingerprint sensors, wherein thepositional relationship between the directions of fingertips of thecenters of the fingerprints of several fingers are used inauthentication.

However, with the information used that is disclosed in InternationalPublication Pamphlet No. WO 2005/069212 and Japanese Laid-open PatentPublication No. 2003-281508, it is difficult to shorten the timerequired for authentication.

SUMMARY

According to an aspect of the invention, a biometric informationprocessing apparatus includes a first aspect of the present disclosure,there is provided a biometric information processing apparatus thatincludes: a biometric sensor that acquires biometric information of afirst instance, a second instance and a third instance; anauthentication feature extraction unit that extracts an authenticationfeature for matching based on the biometric information of each of thesecond instance and the third instance; a normalization unit thatnormalizes the relative position between the authentication features ofthe second instance and the third instance by using the biometricinformation of the first instance; and a relative feature extractionunit that extracts a relative feature indicating a relative positionalrelationship between the authentication features of the second instanceand the third instance normalized by the normalization unit.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims. It is to be understood that both the foregoing generaldescription and the following detailed description are exemplary andexplanatory and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram for describing a hardware configuration of abiometric information processing apparatus according to a firstexemplary embodiment, and FIG. 1B is a schematic diagram of a biometricsensor of the present disclosure.

FIG. 2 is a block diagram of each function implemented by the executionof a biometric information processing program.

FIG. 3 is a block diagram of each function implemented during abiometric data registration process.

FIG. 4 is a view for describing an example of a flowchart executedduring a biometric data registration process.

FIG. 5 is a view illustrating an example of a flowchart representing anormalization process.

FIG. 6 is a view illustrating an example of a palm image.

FIG. 7 is a view illustrating another example of a palm image.

FIG. 8 is a view illustrating yet another example of a palm image.

FIG. 9 is a view illustrating still yet another example of a palm image.

FIGS. 10A and 10B are views illustrating an example of a coordinatesystem when the position of a center of a fingerprint is used as theorigin.

FIG. 11 is a view illustrating an example of a data structure ofrelative features calculated using the position of the center of afingerprint of each of index finger, middle finger and ring finger asthe origin.

FIG. 12 is a block diagram of each function implemented during abiometric authentication process.

FIG. 13 is a view for describing an example of a flowchart executedduring the biometric authentication process.

FIG. 14 is a flowchart for describing the details of a positionalalignment process as in S26 and a matching process as in S27.

FIG. 15 is flowchart for describing another example of a biometricauthentication process.

FIG. 16 is another block diagram of each function implemented by theexecution of the biometric information processing program.

FIG. 17 is another block diagram of each function implemented during thebiometric authentication process.

FIG. 18 is a view for describing another example of a flowchart executedduring the biometric authentication process.

FIG. 19 is a view for describing another example of normalization.

FIG. 20 is a view for describing an example of normalization using thefirst joint of a finger.

FIG. 21 is a view illustrating an example in which a relative feature isextracted based on both the veins and fingerprint of each finger.

FIG. 22 is a view illustrating an example of a data structure forrelative features.

FIG. 23 is a view for describing an example of normalization using theiris and the face.

DESCRIPTION OF EMBODIMENTS

First, the terms used in the following embodiments will be described. Aninstance refers to a unit of a living body used in authentication, suchas for example, a finger, a palm, a face or an eye. Accordingly, afinger and a palm are different instances. Furthermore, a middle fingerand an index finger are also different instances, and a right eye and aleft eye are also different instances. Modality refers to the type of abiometric feature, such as for example, a fingerprint, a vein, an iris,a face shape, or a palm shape. Accordingly, the fingerprint and the veinfor the same finger are different modalities.

Biometric information refers to information of a body and includes, forexample, a biometric feature representing a biometric characteristic ofan individual body. An authentication feature is data extractedindependently from each biometric feature and used in a matching processfor authentication. For example, the authentication feature includes apattern of a fingerprint image, a positional relationship betweenminutiae of fingerprints, or a pattern of a palm vein image.Alternatively, the authentication feature may also include features usedto narrow candidates for matching. For example, the authenticationfeature may also include a fingerprint pattern along with the pattern ofa fingerprint image, which are used to narrow candidates for matching byusing fingerprint pattern classifications.

A relative feature is information indicating a relative positionalrelationship between the authentication features of different instances.The relative feature is, for example, a distance between the right eyeand the left eye, the coordinate value of the position of the center ofa fingerprint, and minutiae of a fingerprint of a finger, when theposition of the center of the fingerprint of any one of other fingers isused as a reference point. Furthermore, when the biometric informationof the fingertip is finger veins, the relative feature may be, forexample, a relative coordinate value of an intersection point, anendpoint, or a bifurcation point of two finger veins. Furthermore, theauthentication features of other modalities may be the subject of therelative feature. For example, when the position of the center of thefingerprint of a finger is used as a reference point, the relativefeature may be, for example, the intersection point, the endpoint or thebifurcation point of the veins of another finger.

Hereinafter, embodiments of the present disclosure will be describedwith reference to the accompanying drawings.

FIG. 1A is a block diagram for describing the hardware configuration ofa biometric information processing apparatus 100 according to a firstexemplary embodiment. FIG. 1B is a schematic diagram of a biometricsensor that is to be described below. Referring to FIG. 1A, thebiometric information processing apparatus 100 includes, for example, acentral processing unit (CPU) 101, a random access memory (RAM) 102, astorage device 103, a display device 104, a biometric sensor 105, acommunication unit 106, and an identification information acquiring unit107. Each of these components is connected with each other by, forexample, a data bus. Furthermore, the biometric information processingapparatus 100 is connected to an authentication server 200 and adatabase server 300 through a network.

The CPU 101 includes one or more cores. The RAM 102 is a volatile memorywhich temporarily stores, for example, a program executed by the CPU 101or data processed by the CPU 101.

The storage device 103 is a non-volatile storage device. As for thestorage device 103, a read only memory (ROM), a solid state drive (SSD)such as a flash memory, a hard disk driven by a hard disk drive or so onmay be used. A biometric information processing program according to thepresent embodiment is stored in the storage device 103. The displaydevice 104 is, for example, a liquid crystal display or anelectroluminescence panel, and displays, for example, the results ofbiometric information processing.

The biometric sensor 105 is a sensor that detects biometric informationof a user, takes at least three different instances as a subject to bedetected, and acquires the same or different modalities. In the presentembodiment, the biometric sensor 105 acquires a palm image as palminformation while also acquiring fingerprint images of a plurality offingers as fingertip information. That is, the biometric sensor 105takes different instances such as a plurality of fingers and a palm as asubject, and acquires different modalities, such as the fingerprint andthe veins.

Referring to FIG. 1B, the biometric sensor 105 is equipped with afingertip information acquisition unit 108 and a palm informationacquisition unit 109. The fingertip information acquisition unit 108 is,for example, an optical sensor or a capacitive sensor, and acquiresfingerprint images of two or more fingers. Furthermore, either a contacttype sensor or a contactless type sensor may be used as the fingertipinformation acquisition unit 108. When normalizing a positionalrelationship that is to be described below, using a combination ofneighboring fingers is desirable to ensure that positional deviationdoes not occur. Therefore, in the present embodiment, the fingertipinformation acquisition unit 108 acquires the fingerprint images of theindex finger, middle finger and ring finger.

The palm information acquisition unit 109 may be implemented with, forexample, a complementary metal-oxide-semiconductor (CMOS) camera, andacquires the palm image. In the present embodiment, the palm informationacquisition unit 109 acquires a palm image that includes a portioncapable of determining the direction of a finger. The palm image may be,for example, a vein image, a palm shape image, or a palm print image. Byusing near-infrared light the palm information acquisition unit 109 mayinclude the vein image in the palm image.

Meanwhile, it is desirable that the fingertip information acquisitionunit 108 and the palm information acquisition unit 109 are disposed toboth be fixed in order to stably extract biometric information. Inaddition, it is desirable to acquire the fingertip biometric informationand palm biometric information at the same time. For example, it may bedesirable to fix the fingertip information acquisition unit 108 and thepalm information acquisition unit 109 within a given distance L so thata fingerprint image and a palm image can be simultaneously acquired. Thedistance L is, for example, about several cm to 10 cm, which may be fitwithin an average palm size.

The communication unit 106 is, for example, an interface to be coupledto a local area network (LAN). The identification information acquiringunit 107 is an input device, such as, for example, a keyboard or amouse. The biometric information processing apparatus 100 communicatesand exchanges information with an authentication server 200 and adatabase server 300 over a network via the communication unit 106. Theauthentication server 200 includes, for example, a CPU 201, a RAM 202, astorage device 203, and a communication unit 204. The storage device 203stores a matching program. The database server 300 includes, forexample, a storage device 301, and a communication unit 302.

A biometric information processing program stored in the storage device103 of the biometric information processing apparatus 100 is deployed tobe executable in the RAM 102. The CPU 101 executes the biometricinformation processing program deployed in the RAM 102. Accordingly, therespective processes to be performed by the biometric informationprocessing apparatus 100 are executed. Through the execution of thebiometric information processing program, a biometric data registrationprocess and a biometric authentication process are performed. Thebiometric data registration process is a process in which identificationinformation, a quality score, authentication features and relativefeatures (hereinafter, collectively “registration data”) of anunregistered new user (hereinafter, “a user to be registered”) areregistered in a database as data for a matching process for personalauthentication. Biometric authentication is a process in which a user tobe authenticated is specified by personal authentication based onmatching of the identification information, the quality score,authentication features and relative features that are acquired duringthe authentication (hereinafter, collectively “input data”) with theregistration data. In the present embodiment, both authenticationfeatures and relative features are used during the biometricauthentication process so that erroneous authentication is suppressedwhile suppressing the lengthening of the time required forauthentication.

FIG. 2 illustrates a block diagram of each function implemented by theexecution of a biometric information processing program. A qualitydetermination unit 10, a fingertip information normalization unit 20, anauthentication feature extraction unit 30, a relative feature extractionunit 40, a registration unit 50 and an authentication result output unit60 are implemented by the execution of the biometric informationprocessing program. The positional alignment reference positioncalculation unit 210 and a matching processing unit 220 are implementedby the execution of the matching program stored in the storage device203.

(Biometric Data Registration Process)

FIG. 3 illustrates a block diagram of each function implemented during abiometric data registration process. FIG. 4 is a view for describing anexample of a flowchart executed during the biometric data registrationprocess. Hereinafter, an example of the biometric data registrationprocess will be described with reference to FIGS. 3 and 4.

The identification information acquiring unit 107 acquiresidentification information that is input by the user to be registered(S1). The identification information is information that specifies theuser and is, for example, a name, a nickname, or an ID of the user.Subsequently, the biometric sensor 105 acquires the biometricinformation of the user to be registered (S2). Specifically, thefingertip information acquisition unit 108 acquires the fingerprintimages of the index finger, middle finger and ring finger, and the palminformation acquisition unit 109 acquires palm images that include theposition of the base of each finger.

Subsequently, the quality determination unit 10 calculates the qualityscore using at least a portion of the images acquired by the fingertipinformation acquisition unit 108 and the palm information acquisitionunit 109 (S3). A high quality score implies a good state of thebiometric characteristic of the body. In the present embodiment, thequality determination unit 10 calculates the quality score based on thefingerprint images acquired by the fingertip information acquisitionunit 108. In this case, the calculated quality score may be, forexample, a value which is inversely proportional to the amount ofcorrection in the direction of the ridges. In the meantime, the amountof correction in the direction of the ridges has a property indicatingthat if the value is large, the direction of the ridges becomesscattered and the state of the finger image is poor.

Subsequently, the authentication feature extraction unit 30 extracts anauthentication feature used in the matching process from the fingerprintimage (S4). The authentication feature that can be extracted from thefingerprint image is, for example, a ridge pattern of the fingerprint,or a positional relationship between minutiae of the fingerprint. Whenfinger veins are used as the biometric feature of the fingertip, theauthentication feature may be, for example, the pattern of the fingerveins. Furthermore, the authentication feature extraction unit 30 mayextract information used in the matching process based on the palm imageas a portion of the authentication features in order to improve thediscrimination accuracy of the authentication feature. Theauthentication feature that may be extracted from the palm image is, forexample, the pattern of the palm veins.

The fingertip information normalization unit 20 normalizes by moving thefingerprint images of several fingers to a given position (S5). Thefingertip information normalization unit 20 normalizes the plurality ofthe fingerprint images of the finger to a given positional relationshipso that the variation of input position of the finger image may becorrected. Accordingly, information may be extracted without beinginfluenced by variation.

The fingertip information normalization unit 20 performs normalization,for example, by following the flow as illustrated in FIG. 5. First, thefingertip information normalization unit 20 extracts a straight linecorresponding to the direction of each finger based on the outline ofthe vicinity of the base of each finger among the palm image asillustrated in FIG. 6 (S11). Specifically, the fingertip informationnormalization unit 20 defines straight lines representing the directionsof two neighboring fingers as straight lines R1 and R2, as illustratedby the black bold lines in FIG. 7. Subsequently, the fingertipinformation normalization unit 20 extends the two straight lines R1 andR2, respectively, to obtain an angle θ and an intersection point Obetween the two straight lines R1 and R2, as illustrated in FIG. 8(S12). Subsequently, the fingertip information normalization unit 20rotates one of the fingerprint images around the intersection point O bythe angle θ, as illustrated in FIG. 9 (S13). The fingertip informationnormalization unit 20 determines the positional relationship between thefingerprint images obtained by the rotation as a fingerprint image afternormalization.

In the meantime, since the normalization process aims to standardize therelative positions between different fingerprint images, the straightlines R1 and R2 do not need to be parallel with each other. For example,the fingertip information normalization unit 20 may performnormalization by rotating the fingerprint image until the angle betweenthe straight lines R1 and R2 becomes the most frequently input angle inorder to reduce the frequency of the rotation process and optimize thenormalization. Furthermore, rather than rotating the fingerprint image,the fingertip information normalization unit 20 may rotate either theposition of minutiae or the position of the center of the fingerextracted from the authentication feature extraction unit 30 in order toreduce the time required for the rotation process.

Referring back to FIG. 4 again, the relative feature extraction unit 40extracts the relative feature from the fingerprint image afternormalization (S6). As for the coordinate system for the relativefeatures in the present embodiment, the relative feature extraction unit40 uses a Cartesian coordinate system in which the position of thecenter of the fingerprint is set as the origin and the direction of themiddle finger is set as an axis. Furthermore, as for the relativefeatures, only minutiae located within a given range from the origin mayperhaps be used in order to shorten the time required for processingwithout using all the minutiae in the fingerprint image. Furthermore, asfor the origin of the relative features, the positions of the center ofthe fingerprint of several fingers may be used to calculate thecoordinate values of the minutiae points based on the position of thecenter of each fingerprint. The center of the fingerprint indicates theflow of a semicircular shape included in the fingerprint, and may alsobe called a core. In a fingerprint having a whorl pattern, the centralpoint of the whorl is the center of the fingerprint. As a method forcalculating the center of the fingerprint, for example, the technicalcontents disclosed in Japanese Patent Publication No. 2790689 may beused.

An example of a coordinate system in which the positions of the centersof the fingerprint of several fingers are set as the origins isillustrated in FIGS. 10A and 10B. Coordinate values are calculated basedon plural positions of the centers of the fingerprint in order toextract the relative feature even when the acquisition of the positionof the center of the fingerprint of any one finger fails and so that therelative feature may be extracted stably. FIG. 11 illustrates an exampleof a data structure for the relative features calculated using theposition of the center of the fingerprint of each of the index finger,middle finger and ring finger as the origin.

The registration unit 50 registers the acquired identificationinformation, the quality score, the relative features and theauthentication features to a database of the database server 300 asregistration data, as illustrated in FIG. 2 (S7). Referring to FIG. 2,the registration data is associated with each user and then registered.The biometric data registration process is then ended by completing S7.

[Biometric Authentication Process]

When a user who has been registered and is to be authenticated tries tologin to a terminal equipped with, for example, the biometricinformation processing apparatus 100, the biometric informationprocessing apparatus 100 performs a biometric authentication process.FIG. 12 illustrates a block diagram of each function implemented duringthe biometric authentication process. FIG. 13 is a view for describingan example of a flowchart executed during the biometric authenticationprocess. An example of the biometric authentication process will bedescribed hereinafter with reference to FIGS. 12 and 13.

The biometric sensor 105 acquires the biometric information of a user tobe registered according to the same procedure as S2 of FIG. 4 (S21).Subsequently, the quality determination unit 10 performs a qualitydetermination according to the same procedure as S3 of FIG. 4 (S22).Subsequently, the authentication feature extraction unit 30 extracts theauthentication features used in the matching process based on thefingerprint images of several fingers according to the same procedure asS4 of FIG. 4 (S23). Subsequently, the fingertip informationnormalization unit 20 normalizes the fingertip images by moving thefingerprint images of several fingers to a given position according tothe same procedure as S5 of FIG. 4 (S24). The relative featureextraction unit 40 extracts the relative features based on the images ofthe fingerprints after normalization according to the same procedure asS6 of FIG. 4 (S25). The input data acquired in S21 to S25 is sent to theauthentication server 200.

Subsequently, the positional alignment reference position calculationunit 210 of the authentication server 200 performs a positionalalignment process (S26). The positional alignment process is a processthat aligns the position of the authentication features of theregistration data with the position of the authentication features ofthe input data. Authentication precision is improved by means of thepositional alignment process. Furthermore, the positional alignmentprocess may be performed by using relative features. Subsequently, thematching processing unit 220 performs matching of the input data withthe registration data for each finger based on the result of thepositional alignment process (S27). In this case, matching is a processin which the matching score between the authentication feature of theregistration data and the authentication feature of the input data iscalculated. A high matching score represents a high degree of similarityfor the authentication features of the registration data and theauthentication features of the input data.

FIG. 14 is a flowchart for describing details of the positionalalignment process of S26 and the matching process of S27. In the presentembodiment, the processing of FIG. 14 is performed for each fingerprintimage. Referring to FIG. 14, the positional alignment reference positioncalculation unit 210 first determines corresponding pairs of eachminutiae of the input data and each minutiae of the registration data(S31). Specifically, the positional alignment reference positioncalculation unit 210 prepares pairs of each minutiae of the input dataand each minutiae of the registration data so that the distance of acoordinate value among the relative features calculated from the samereference position becomes the shortest. The distance is, for example,Euclidean distance.

Subsequently, the positional alignment reference position calculationunit 210 determines whether there is a pair of which the distance of therelative feature is a given value or less among the pairs (S32). Whenthe determination result is “Yes” at S32, the positional alignmentreference position calculation unit 210 determines the center of thegravity of the pair as a reference position for positional alignment(S33). When there are a plurality of pairs of which the distance of therelative feature of each is a given value or less, the positionalalignment reference position calculation unit 210 may calculate, foreach of the input data and the registration data, the position of thecenter of the gravity of minutiae selected as the pair as the referenceposition. When the determination result at S32 is “No”, the positionalalignment reference position calculation unit 210 determines that thereference position for positional alignment is absent (S34).

In the meantime, the positional alignment reference position calculationunit 210 may perhaps determine the reference position by using only thevalue of the relative feature calculated based on the position of thecenter of the fingerprint of the fingerprint image of a finger of whichthe quality score of the input data is a given value or more.Alternatively, the positional alignment reference position calculationunit 210 may perhaps use only a value of the relative feature calculatedbased on the position of the center of the fingerprint of thefingerprint image of a finger of which the quality score is the highestamong the three fingers of the input data. Furthermore, the positionalalignment reference position calculation unit 210 may possibly use onlya value of the relative feature calculated based on a position of thecenter of the fingerprint of the finger for which sum of the qualityscores is a given value or more when comparing the quality scores of thesame finger between the input data and the registration data. By using astable reference position obtained using a fingerprint image having agood fingertip state, a stable positional alignment process may beperformed without spending too much time, even for a fingerprint imagethat does not have a good fingertip state and does not have gooddetection precision for the reference position for positional alignment.

After S34, the matching processing unit 220 overlaps the input data andthe registration data with each other based on the positionalrelationship after normalization obtained by the fingertip informationnormalization unit 20 (S36). For example, the matching processing unit220 overlaps the input data and the registration data with each otheraccording to a coordinate system based on the position of the center ofthe fingerprint of a finger.

Conversely, after S33, the matching processing unit 220 overlaps theinput data and the registration data with each other by overlapping thereference positions obtained at S33 (S35). Using a reference positioncalculated by a plurality of corresponding minutiae positions, the inputdata and the registration data may be overlapped at a position having asmaller error than when the reference position is not used.

After performing either S35 or S36, the matching processing unit 220performs positional alignment of the input data and the registrationdata (S37). Specifically, the matching processing unit 220 performs fineadjustment, such as rotation within a given angle α or movement within agiven range S, with respect to the input data and the registration dataoverlapped with each other, so that the number of matching minutiaebecomes the largest. Alternatively, the matching processing unit 220 mayperform fine adjustment so that ridge patterns match each other as muchas possible. For example, the α value is 10 (ten) degrees or less andthe range of S is several square millimeters or less. Since thedeviation of the reference position becomes small due to thenormalization by the fingertip information normalization unit 20, therange for fine adjustment may be narrowed. The overall time required forthe matching process may be shortened by narrowing the range of the fineadjustment.

Subsequently, the matching processing unit 220 calculates the matchingscore with respect to the authentication features of the input data andregistration data after positional alignment (S38). The matching scoreis the degree of similarity of the authentication features. For example,using the authentication features, the matching processing unit 220calculates the degree of similarity of the ridge patterns, or the degreeof coincidence of the position or type of minutiae or the like as thematching score. In the meantime, since the processing of FIG. 4 isperformed for each finger, the matching score of each finger iscalculated.

Referring back to FIG. 13, the matching processing unit 220 determineswhether the largest matching score is greater than or equal to athreshold (S28). When the determination result is “Yes” at S28, theauthentication result output unit 60 receives the determination resultand outputs a result indicating that authentication succeeded (S29).When the determination result is “No” at S28, the authentication resultoutput unit 60 receives the determination result and outputs a resultindicating that authentication failed (S30). After either S29 or S30 hasbeen performed, the execution of flowchart of FIG. 13 ends.

According to the present embodiment, a relative feature having smallpositional deviation may be extracted by the normalization process. Inthis case, the search range for the positional alignment for thematching target may be narrowed. Accordingly, since the processing timerequired for positional alignment may be reduced, the overall matchingtime may be reduced as well. That is, according to the presentembodiment, information that can shorten the time required forauthentication may be extracted. In particular, the effect of theprocessing time reduction is substantial in one-to-N authenticationwhere a matching of a user to be authenticated needs to be conductedwith multiple registered users. Furthermore, even if the state of thebody to be detected is poor, the influence of the poor state of the bodymay be reduced by using the relative feature of a body having a goodstate.

Modified Example

FIG. 15 illustrates a flowchart for describing another example of thebiometric authentication process. S41 to S47 of FIG. 15 are the same asS21 to S27 of FIG. 13. After performing S47, the matching processingunit 220 determines whether there is a user whose matching score is afirst threshold or more (S48). When the determination result is “Yes” atS48, the matching processing unit 220 determines whether there is only asingle user for which the matching score is the first threshold or more(S49). When the determination result is “Yes” at step 49, theauthentication result output unit 60 outputs a result indicating thatauthentication succeeded (S50). Subsequently, the execution of theflowchart of FIG. 15 ends. When the determination result is “No” at S48,the authentication result output unit 60 outputs a result indicatingthat authentication failed (S53). Subsequently, the execution of theflowchart of FIG. 15 ends.

When the determination result is “No” at S49, the matching processingunit 220 performs a matching process using the relative feature (S51).For example, when the relative feature is the positional relationshipbetween minutiae, the matching processing unit 220 calculates a degreeof similarity for the positional relationship between minutiae withrespect to registration data that has a matching score greater than orequal to the first threshold value. The degree of similarity PS(Ti,I)between the registration data Ti and the input data I is calculated bythe following equation 1.

$\begin{matrix}{{{{PS}\left( {T_{i},I} \right)} = {\sum\limits_{{({x_{j},y_{j}})} \in {R{(I)}}}{F\left( {x_{j},y_{j},T_{i}} \right)}}}{where}{{F\left( {x_{j},y_{j},T_{i}} \right)} = \left\{ {{\begin{matrix}\alpha & \left( {\exists{\left( {x_{k}^{\prime},y_{k}^{\prime}} \right) \in {{T_{i}\mspace{14mu}{s.t.\mspace{14mu}{d\left( {x_{j},y_{j},x_{k}^{\prime},y_{k}^{\prime}} \right)}}} < \delta}}} \right) \\{- \beta} & ({otherwise})\end{matrix}{d\left( {x_{j},y_{j},x_{k}^{\prime},y_{k}^{\prime}} \right)}} = \sqrt{\left( {x_{j} - x_{k}^{\prime}} \right)^{2} + \left( {y_{j} - y_{k}^{\prime}} \right)^{2}}} \right.}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack\end{matrix}$

Ti represents registration data, I represents input data, PS(Ti,I)represents the degree of similarity between the registration data Ti andthe input data I, and R(I) represents the relative feature of the inputdata. In addition α>0, β>0, δ>0.

The matching processing unit 220 determines whether the maximum value ofthe degree of similarity calculated from the equation 1 is greater thanor equal to a second threshold value (S52). When the determinationresult is “Yes” at S52, the authentication result output unit 60performs S50. When the determination result is “No” at S52, theauthentication result output unit 60 performs S53.

When there are several users having a matching score that is greaterthan or equal to the first threshold value, the possibility of erroneousauthentication becomes more likely. However, erroneous authenticationmay be suppressed by performing a second determination using therelative feature. With one-to-one authentication that presupposes thematching of a single user to be authenticated and a single registereduser, when the matching score is both greater than or equal to the firstthreshold value and less than a third threshold value, the matchingprocessing unit 220 may calculate the degree of similarity according toequation 1 by using the relative feature. When the matching score isgreater than or equal to the third threshold value, or when the matchingscore is greater than or equal to the first threshold value and thedegree of similarity is greater than or equal to the second thresholdvalue, the matching processing unit 220 calculates an authenticationresult indicating that authentication succeeded. When the matching scoreis less than the third threshold value but is greater than or equal tothe first threshold value, this may be an exceedingly rare case of wherethe registration data used to match the user is the registration data ofanother user. In this case, the matching processing unit 220 may performa second determination by using the relative features as well tosuppress erroneous authentication.

Second Embodiment

The candidates for matching may be narrowed during a matching process.For example, referring to FIG. 16, the positional alignment referenceposition calculation unit 210, the matching processing unit 220, and acandidate for matching narrowing unit 230 may be implemented byexecuting the matching program stored in the storage device 203 of theauthentication server 200. FIG. 17 is a block diagram of each functionimplemented during the biometric authentication process. FIG. 18 is aview for describing an example of a flowchart executed during thebiometric authentication process. Below, an example of the biometricauthentication process will be described with reference to FIGS. 17 and18.

S61 to S65 of FIG. 18 are the same as S21 to S25 of FIG. 13.Furthermore, S68 to S70 of FIG. 18 are the same as S28 to S30 of FIG.13.

After performing S65, the matching candidate narrowing unit 230 narrowsthe candidates for matching (S66). Specifically, the matching candidatenarrowing unit 230 calculates a narrowed score in which, when comparingthe relative feature of the registration data with the relative featureof the input data, the probability that the registration data and theinput data correspond to data about the same person becomes higher asthe narrowed score becomes higher. For example, the narrowed score iscalculated from a value indicating whether the narrowed score, whichcorresponds to the positional relationship of minutiae among therelative features in the input data, is present in the positionalrelationship between minutiae among the relative features in theregistration data. The narrowed score may be calculated according toequation 1. The matching candidate narrowing unit 230 determinesregistration data having a narrowed score that is greater than or equalto a given numerical value among registration data as the candidates formatching. Alternatively, the matching candidate narrowing unit 230 listsregistration data in descending order of narrowed score and determines agiven number of the registration data with higher rankings as thecandidates for matching.

The matching processing unit 220 matches the input data against theregistration data for the candidates for matching. That is, the matchingprocessing unit 220 completes the matching process without matching allcandidates for matching against the input data. Accordingly, the timerequired for matching may be reduced. In the meantime, through thenormalization process of the fingertip information normalization unit20, the relative feature becomes a stable amount of feature having asmall deviation. Accordingly, the relative feature may be used in anarrowing process without performing positional alignment. Accordingly,the narrowing process may be performed within a shorter time than aprocess which requires positional alignment.

Other Embodiment

In each embodiment as described above, although one-to-N authenticationis performed where matching of a user to be authenticated is performedagainst multiple registered users, one-to-one authentication may also beperformed where the matching of a single user to be authenticated isperformed against a single registered user. One-to-one authenticationmay be implemented by limiting the number of registered users formatching by using identification information acquired from the users tobe authenticated during the biometric authentication process.Alternatively, when the registration data for only one person has beenregistered, one-to-one authentication may be implemented.

In the embodiment as described above, the biometric informationprocessing apparatus 100, the authentication server 200, and thedatabase server 300 are configured as separate apparatuses, but may beconfigured as, for example, a single server. In this case, the matchingprogram may be included in the biometric information processing program.Furthermore, the positional alignment reference position calculationunit 210, the matching processing unit 220 and the matching candidatenarrowing unit 230 may be implemented as functions within the biometricinformation processing apparatus 100.

Subjects to be normalized may possibly not have the same modality. Forexample, the vein image and the fingerprint image may be normalized.FIG. 19 is a view for describing another example of normalization. Theexample in FIG. 19 illustrates the normalization of the position of thefinger veins of a middle finger and the position of the fingerprintimage of a ring finger. Referring to FIG. 19, by using the processes ofFIG. 5 to FIG. 9, one of the images is rotated so that the positionalrelationship between the finger images may be normalized. In this case,the fingertip information normalization unit 20 may set a coordinatesystem in which the origin is the center of the fingerprint and an axisis the direction of the fingertip in order to extract as relativefeatures, for example, the pattern of the finger vein, the bifurcationpoint, and the endpoint of the finger vein.

Alternatively, when the first joint of a finger is present in the image,the fingertip information normalization unit 20 may use the first joint.FIG. 20 is a view for describing an example of normalization using thefirst joint. Referring to FIG. 20, the fingertip informationnormalization unit 20 may set a coordinate system in which, in order toextract finger veins, the origin is the center of the first joint of afinger and an axis is the first joint, in order to extract thecoordinates of minutiae of fingerprint of the ring finger as therelative feature.

Furthermore, both the veins and the fingerprint of each finger may beused. FIGS. 21 and 22 illustrate an example in which relative featuresare extracted from both the veins and the fingerprint of each finger.For example, the fingertip information normalization unit 20 may extractthe pattern of the finger veins, the bifurcation point and the endpointof the finger veins to use these information as the relative featuresalong with the relative feature of the fingerprint in the coordinatesystem which is based on the position of the center of the fingerprint.

In each embodiment as described above, the finger and the palm are usedas different instances, but other instances may also be used. FIG. 23illustrates an example in which the iris and the face are used asinstances. Referring to FIG. 23, iris images of both eyes are acquired.Subsequently, the iris images of both eyes are normalized by correctingthe face image to a frontal face image. In the meantime, the iris imagesof both eyes may be further normalized so that the line segmentconnecting the centers of both eyes becomes horizontal. During thebiometric data registration process, authentication features may beextracted from the normalized images. Furthermore, the length L of theline segment may be used as a relative feature. During theauthentication process, the iris image of the input data may be expandedor contracted so that the length of the relative feature of theregistration data becomes the same as the length of the relative featureof the input data. A matching process may be performed based on the irisby extracting the authentication feature from the acquired iris image.In this case, rectification may be made even if the scale or gradient ofthe iris image varies when the distance between the face and the cameraare different or the gradient of the face is different during theregistration process and during the authentication process.

A recording medium storing a software program for implementing thefunction of the biometric information processing apparatus 100 may beprovided in the biometric information processing apparatus 100 and theCPU 101 may execute the software program. The recording medium forproviding the program includes, for example, a CD-ROM, a DVD, a Blue-rayDisc, or a SD card. Furthermore, in each embodiment as described above,the respective functions are implemented by the execution of the programby the CPU, but each embodiment is not limited thereto. For example, therespective functions may be implemented by using, for example, adedicated circuit.

As described above, the embodiments according to the present disclosurehas been described in detail, but the present disclosure is not limitedto a specific embodiment, and various alterations and changes may bemade without departing from the gist of the exemplary embodiments of thepresent disclosure.

According to a biometric information processing apparatus, a biometricinformation processing method and a recording medium thereof storing abiometric information processing program disclosed in the presentdisclosure, information capable of shortening the time required for anauthentication process may be extracted.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the inventionand the concepts contributed by the inventor to furthering the art, andare to be construed as being without limitation to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although the embodiment(s) of the presentinvention has (have) been described in detail, it should be understoodthat the various changes, substitutions, and alterations could be madehereto without departing from the spirit and scope of the invention.

What is claimed is:
 1. A biometric information processing apparatus,comprising: a biometric sensor configured to acquire biometricinformation of a first instance, a second instance and a third instance;a processor configured to execute a process, the process comprising:extracting an authentication feature used for matching from thebiometric information of each of the second instance and the thirdinstance; normalizing relative positions of authentication features ofthe second instance and the third instance by using the biometricinformation of the first instance; extracting a relative featureindicating a relative positional relationship between the authenticationfeatures of the second instance and the third instance after therelative positions of the authentication features of the second instanceand the third instance have been normalized; narrowing out a matchingcandidate from a plurality of users registered in a database by matchingrelative features of the plurality of users with the extracted relativefeature; and authenticating a user according to a result of matching theauthentication feature and the relative feature for the matchingcandidate narrowed in the narrowing, among the authentication featuresand the relative features of the plurality of users registered in thedatabase, with the authentication feature extracted in the extracting ofthe authentication feature and the relative feature extracted in theextracting of the relative feature.
 2. The biometric informationprocessing apparatus according to claim 1, wherein the processorconfigured to execute the process further including: acquiringidentification information input by a user, wherein the authenticatingprocedure including specifying the relative feature and theauthentication feature in association with the identificationinformation in the database to authenticate a user according to a resultof matching of the specified relative feature and authentication featurewith the relative feature extracted in the extracting procedure of therelative feature and the authentication feature extracted in theextracting procedure of the authentication feature.
 3. The biometricinformation processing apparatus according to claim 1, wherein theprocessor configured to execute the process further including:calculating a reference position for positional alignment between theauthentication feature registered with the database and theauthentication feature extracted in the extracting of the authenticationfeature using the relative feature, wherein the authenticating includesperforming the positional alignment between the authentication featureregistered with the database and the authentication feature extracted inthe extracting of the authentication feature using the referenceposition.
 4. The biometric information processing apparatus according toclaim 1, wherein the authenticating process includes incorporating intoa matching result a degree of similarity between the relative featuresregistered with the database and the relative feature extracted in theextracting of the relative feature.
 5. The biometric informationprocessing apparatus according to claim 1, wherein the second instanceand the third instance are different fingers and the first instance is apalm.
 6. The biometric information processing apparatus according toclaim 5, wherein the normalizing includes normalizing a direction alongwhich the different fingers extend based on the direction of the base ofeach finger of the palm.
 7. The biometric information processingapparatus according to claim 6, wherein the biometric information of thesecond instance and the third instance are fingerprint images of severalfingers, and the relative feature is a relative position between acenter of fingerprint of one finger and minutiae of the other finger. 8.The biometric information processing apparatus according to claim 1,wherein the biometric sensor further acquires biometric information of afourth instance, and wherein the extracting of the authenticationfeature includes extracting an authentication feature for matching fromthe biometric information of the fourth instance, and the normalizingincludes normalizing a relative position between the authenticationfeatures of second instance, the third instance and the fourth instanceusing the biometric information of the first instance, and theextracting of the relative feature includes extracting a relativefeature indicating a relative positional relationship between theauthentication features of the second instance, the third instance andthe fourth instance normalized in the normalizing, and wherein theprocessor configured to execute the process further including:determining quality scores of the biometric information of the secondinstance, the third instance and the fourth instance, and wherein theauthenticating does not use a relative feature calculated based on aninstance of which the quality score obtained in the procedure is a givenvalue or less.
 9. A biometric information processing method, comprising:acquiring biometric information of a first instance, a second instanceand a third instance; extracting an authentication feature for matchingfrom the biometric information of each of the second instance and thethird instance; normalizing a relative position between authenticationfeatures of the second instance and the third instance by using thebiometric information of the first instance; extracting a relativefeature indicating a relative positional relationship between theauthentication features of the second instance and the third instancenormalized in the normalizing; narrowing out a matching candidate from aplurality of users registered in a database by matching relativefeatures of the plurality of users with the extracted relative feature;and authenticating a user according to a result of matching theauthentication feature and the relative feature for the matchingcandidate narrowed in the narrowing, among the authentication featuresand the relative features of the plurality of users registered in thedatabase, with the authentication feature extracted in the extracting ofthe authentication feature and the relative feature extracted in theextracting of the relative feature.
 10. The biometric informationprocessing method according to claim 9, further comprising: acquiringidentification information input by a user, wherein the authenticatingincludes specifying the relative feature and the authentication featurein association with the identification information in the database toauthenticate a user according to a result of matching of the specifiedrelative feature and authentication feature with the relative featureextracted in the extracting of the relative feature and theauthentication feature extracted in the extracting of the authenticationfeature.
 11. The biometric information processing method according toclaim 9, further comprising: calculating a reference position forpositional alignment between the authentication feature registered withthe database and the authentication feature extracted in the extractingof the authentication feature using the relative feature, wherein theauthenticating includes performing the positional alignment between theauthentication feature registered with the database and theauthentication feature extracted in the extracting of the authenticationfeature using the reference position.
 12. The biometric informationprocessing method according to claim 9, wherein the authenticatingincludes incorporating into a matching result a degree of similaritybetween the relative features registered with the database and therelative feature extracted in the extracting of the relative feature.13. The biometric information processing method according to claim 9,wherein the second instance and the third instance are different fingersand the first instance is a palm.
 14. The biometric informationprocessing method according to claim 13, wherein the normalizingincludes normalizing a direction along which the different fingersextend based on the direction of the base of each finger of the palm.15. A non-transitory computer-readable recording medium storing aprogram causing a computer to execute a process, the process comprising:acquiring biometric information of a first instance, a second instanceand a third instance; extracting an authentication feature used formatching from the biometric information of the second instance and thethird instance; normalizing a relative position of authenticationfeatures of the second instance and the third instance by using thebiometric information of the first instance; and extracting a relativefeature indicating a relative positional relationship between theauthentication features of the second instance and the third instancenormalized in the normalizing; narrowing out a matching candidate from aplurality of users registered in a database by matching relativefeatures of the plurality of users with the extracted relative feature;and authenticating a user according to a result of matching theauthentication feature and the relative feature for the matchingcandidate narrowed in the narrowing, among the authentication featuresand the relative features of the plurality of users registered in thedatabase, with the authentication feature extracted in the extracting ofthe authentication feature and the relative feature extracted in theextracting of the relative feature.
 16. The non-transitorycomputer-readable recording medium according to claim 15, wherein theprocess further comprises: acquiring identification information input bya user, wherein the authenticating includes specifying the relativefeature and the authentication feature in association with theidentification information in the database to authenticate a useraccording to a result of matching of the specified relative feature andauthentication feature with the relative feature extracted in theextracting of the relative feature and the authentication featureextracted in the extracting of the authentication feature.
 17. Thenon-transitory computer-readable recording medium according to claim 15,wherein the process further comprises: calculating a reference positionfor positional alignment between the authentication feature registeredwith the database and the authentication feature extracted in theextracting of the authentication feature using the relative feature,wherein the authenticating includes performing the positional alignmentbetween the authentication feature registered with the database and theauthentication feature extracted in the extracting of the authenticationfeature using the reference position.
 18. The non-transitorycomputer-readable recording medium according to claim 15, wherein theauthenticating includes incorporating into a matching result a degree ofsimilarity between the relative features registered with the databaseand the relative feature extracted in the extracting of the relativefeature.
 19. The non-transitory computer-readable recording mediumaccording to claim 15, wherein the second instance and the thirdinstance are different fingers and the first instance is a palm.